The present invention relates generally to signal testing, and particularly to performance characterization of multi-valued functions, such as digital communication signals.
Periodic signals are easily evaluated on, for example, an oscilloscope where the oscilloscope triggers a display sweep relative to a voltage level of the signal under test, or relative to a trigger signal in phase with the signal under test. Because the signal is cyclic and each display screen begins at the same point in the signal cycle, the same display pattern, i.e., the same portion of the signal cycle, appears on the oscilloscope for each display sweep. The display pattern thereby presents for analysis a stable image of the signal cycle.
A digital communication signal is not so easily evaluated on an oscilloscope. A digital communication signal is typically a bi-state signal, but is not cyclic. The signal changes state in accordance with the stream of data it carries, and for purposes of analysis might just as well be a random pattern of state transitions. When applied to an oscilloscope, such a signal will not produce single-valued wave form, i.e., such as a clock signal which has a single valid voltage for each time ordinate. Therefore, traditional waveform analysis cannot be applied thereto. Each display sweep presents a different portion of essentially a random pattern of state transitions. The display image, repeating display sweeps of different portions of the signal, provides a multi-valued picture of the signal and provides what is called "eye pattern".
In an eye pattern, the high and low states of the signal appear as substantially horizontal traces across the oscilloscope screen. State transitions are not completely random. Each transition potentially occurs relative to a conversion clock signal. Thus, if a state transition occurs, it happens at a known time relative to the conversion clock signal. By triggering the oscilloscope with the conversion clock signal, the state transitions of the communication signal appear at substantially the same locations on the oscilloscope display. The resulting pattern is an open area, the eye, bounded on the top and bottom by the high and low state levels, respectively, and on each side by state transition traces. The state transitions on each side of the opening generally cross and form an "X" shape on each side of the eye. One component of each "X" shape corresponds to high-to-low transitions and the other component corresponds to low-to-high transitions. The distance between the high and low states, the slope of the state transition, overall size of the open area, and other similar measurements aid in characterizing the communication signal producing the eye pattern.
A manual method of characterizing an eye pattern is known. According to this method, one generates an eye pattern on the oscilloscope screen, marks the screen with a grease pencil to identify certain portions of the eye pattern, and visually measures relative positions of the identified portions. While such information provides a basis for characterizing the signal, it must be gathered manually and, therefore, comes with the expense of human labor and the possibility of human error.
Due to the random nature of a digital communication signal, automation of signal characterization is not possible according to known methods as applied to cyclic signals. Accordingly, it would be desirable to provide an automatic method of characterizing digital communication signal analysis.